PDSNet: Patient-Disease Dual Spatial Similarity Neural Networks for Predicting Heart Failure Risk Using Short Electronic Health Records

Heart failure (HF) is a complex and heterogeneous syndrome caused by diverse factors, such as atrial fibrillation, diabetes, and pulmonary hypertension. The intricate pathophysiology of HF, coupled with variability in patient demographics and presentations, poses significant challenges to the effectiveness of existing deep learning models in HF risk prediction. In this paper, we propose a novel deep neural network, PDSNet, which leverages a new dual patient-disease spatial similarity strategy to improve HF risk prediction using short electronic health records. First, we develop ontology graphs to capture hierarchical relationships between patients based on HF-related symptoms and causes; Then, a bipartite graph model is utilized to learn spatial similarities between patients with similar hospital visit histories; Finally, we design a transformer-based architecture to integrate both temporal and spatial dynamics for predicting future hospital visits associated with HF risk. We benchmarked the PDSNet on predicting HF risk for 7,346 patients from the MIMIC-III dataset. Compared to seven state-of-the-art deep learning methods, our PDSNet model achieved improvements of 2%–12% in the area under the curve (AUC) score and 3%–18% in the F1 score. These findings highlight the promising potential of our proposed PDSNet to provide accurate and robust HF risk predictions, paving the way for efficient clinical decision support and personalized HF management.